Investigations on Influence of Bio-Geo Interface on Suction Characteristics of Fine-Grained Soil

Dependence of soil suction on factors such as formation of the soil, its particle size, mineralogy, pore-size distribution characteristics, water content, fabric, stress history and presence of salts has been extensively investigated by researchers. However, the influence of the microbial presence and its activity on the soil suction has not yet received researchers’ attention. With this in view, a methodology that facilitates investigation regarding the presence of ‘soil-microbial interface’, which has been termed as ‘bio-geo interface’, has been proposed in this study. Furthermore, the influence of this ‘interface’ on suction characteristics of such soils has also been investigated and the utility of the proposed methodology in addressing such issues, successfully, has been demonstrated.     

Semitransparent cirrus clouds in the tropical tropopause layer during two contrasting seasons

Association between semitransparent cirrus (STC) and tropospheric dynamics during the two contrasting seasons (Summer-monsoon and Winter) is studied using lidar and MST radar observations at Gadanki (13.5°N, 79.2°E) along with data from Geo-stationary satellite (KALPANA-1). The tropical tropopause layer (TTL) is found to be very conducive for the formation of STC. For those thicker STCs forming within TTL, the cloud top remains mostly steady with the top of the TTL, while the cloud base varies significantly in accordance with the altitude extent of tropospheric convective outflow. Based on depolarization characteristics of STCs (from lidar) along with regional distribution of tropospheric clouds derived from satellite data, altitude profile of horizontal wind, wind field at 150 hPa and air mass back trajectories, it can be reasonably inferred that, while the multi-layered and highly structured STCs during the monsoon period originate mostly from the deep convections, the sub-visual/ultra thin STCs during winter could mostly be of in situ origin.     

Assessing coastal landscape vulnerability using geospatial techniques along Vizianagaram–Srikakulam coast of Andhra Pradesh,India

Vizianagaram–Srikakulam coastal shoreline consisting of beaches, mangrove swamps, tidal channel and mudflats is one of the vulnerable coasts in Andhra Pradesh, India. Five site-specific parameters, namely rate of geomorphology, coastal elevation, coastal slope, shoreline change and mean significant wave height, were chosen for constructing coastal vulnerability index and assessing coastal landscape vulnerability. The findings revealed a shift of 2.5 km in shoreline towards the land surface because of constant erosion and that of 1.82 km towards the sea due to accretion during 1997–2017. The rate of high erosion was found in zones IV and V, and high accretion was found in zones II and III. Coastal vulnerability index analysis revealed constant erosion along shoreline and sea level rise in the study area. Most of the coast in zone V has recorded very high vulnerability due to erosion, high slope, significant wave height and sea level rise. Erosion and accretion, significant wave height, sea level rise and slope are attributed to high vulnerability in zones III and IV. Zone II recorded moderate vulnerability. Relatively lower slope, mean sea wave height and sea level rise have made this zone moderately vulnerable. Very low vulnerability was found in zone I, and low vulnerability was recorded in zone II. Accretion, low slope and low sea level rise were found to be causative factors of lower vulnerability. Thus, zones III, IV and V should be accorded higher priorities for coastal management. The findings can be helpful in coastal land planning and management and preparing emergency plans of the coastal ecosystems.     

Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis

Drought hotspot identification requires continuous drought monitoring and spatial risk assessment. The present study analysed drought events in the agriculture‐dominated mid‐Mahanadi River Basin in Odisha, India, using crop water stress as a drought indicator. This drought index incorporated different factors that affect crop water deficit such as the cropping pattern, soil characteristics, and surface soil moisture. The drought monitoring framework utilized a relevance vector machine model‐based classification that provided the uncertainty associated with drought categorization. Using the proposed framework, drought hotspots are identified in the study region and compared with indices based on precipitation and soil moisture. Further, a bivariate copula is employed to model the agricultural drought characteristics and develop the drought severity–duration–frequency (S–D–F) relationships. The drought hotspot maps and S–D–F curves are developed for different locations in the region. These provided useful information on the site‐specific drought patterns and the characteristics of the devastating droughts of 2002 and 2012, characterized by an average drought duration of 7 months at several locations. The site‐specific risk of short‐ and long‐term agricultural droughts are then investigated using the conditional copula. The results suggest that the conditional return periods and the S–D–F curves are valuable tools to assess the spatial variability of drought risk in the region.     

Assessment of hydrologic impacts of climate change in Tunga–Bhadra river basin,India with HEC‐HMS and SDSM

Climate change would significantly affect many hydrologic systems, which in turn would affect the water availability, runoff, and the flow in rivers. This study evaluates the impacts of possible future climate change scenarios on the hydrology of the catchment area of the Tunga–Bhadra River, upstream of the Tungabhadra dam. The Hydrologic Engineering Center's Hydrologic Modeling System version 3.4 (HEC‐HMS 3.4) is used for the hydrological modelling of the study area. Linear‐regression‐based Statistical DownScaling Model version 4.2 (SDSM 4.2) is used to downscale the daily maximum and minimum temperature, and daily precipitation in the four sub‐basins of the study area. The large‐scale climate variables for the A2 and B2 scenarios obtained from the Hadley Centre Coupled Model version 3 are used. After model calibration and testing of the downscaling procedure, the hydrological model is run for the three future periods: 2011–2040, 2041–2070, and 2071–2099. The impacts of climate change on the basin hydrology are assessed by comparing the present and future streamflow and the evapotranspiration estimates. Results of the water balance study suggest increasing precipitation and runoff and decreasing actual evapotranspiration losses over the sub‐basins in the study area. Copyright © 2012 John Wiley & Sons, Ltd.     

Predictor selection for streamflows using a graphical modeling approach

Streamflows are influenced by various hydroclimatic variables in complex ways. Accurate prediction of monthly streamflows requires a clear understanding of the dependence patterns among these influencing variables and streamflows. A graphical modeling technique, employing conditional independence, is adopted in this study to quantify the interrelationships between streamflows and a suite of available hydroclimatic variables, and to identify a reduced set of relevant variables for parsimonious model development. The nodes in the undirected graph represent relevant variables, and the strengths of the connections among the variables are learnt from the data. The graphical modeling approach is compared to the state-of-the-art method for predictor selection based on partial mutual information. For a synthetic benchmark dataset and a watershed in southern Indiana, USA, the graphical modeling approach shows more discriminating results while being computationally efficient. Along with artificial neural networks and time series models, results of the graphical model are used for formulating a variational relevance vector machine to predict monthly streamflows and perform probabilistic classification of hydrologic droughts in the watershed being studied. The parsimonious models developed for prediction at different lead times performed as well as the non-parsimonious models during both the calibration and testing periods. Drought forecasting for the study watershed at 1-month lead time was performed using the two selected predictors—soil moisture and precipitation anomalies alone, and the model performance was evaluated. The graphical model shows promise as a tool for predictor selection, and for aiding parsimonious model development applications in statistical hydrology.     

Assessment of groundwater quality with special emphasis on nitrate contamination in parts of Gautam Budh Nagar district,Uttar Pradesh,India

Progressive developments in industrial and agricultural activities are causing a critical stress on groundwater quality in developing countries. The objective of this paper is to assess and evaluate the contamination level of groundwater caused by leachate in 11 villages of the Gautam Budh Nagar district in Uttar Pradesh, India. We systematically sampled 22 groundwater samples and 13 leachate samples to ascertain the source of pollution on groundwater quality. The standard analytical methods given by the American Public Health Association (APHA) (Standard methods for examination of water and wastewater, 23rd edn. APHA, AWWA, WPCF, Washington, 2017) were used for quantitative estimation of hydrochemical parameters of collected samples. The results of the analysis of groundwater samples indicate that pH values range from 7.31 to 8.97. The mean magnesium concentration in groundwater samples is 58.93 ± 21.44 mg/L. Out of the groundwater samples taken, approximately 41% and 73% of samples analysis results have been found beyond the acceptable limit with respect to the parameters of turbidity and total dissolved solids, respectively, according to the Bureau of Indian Standards (Indian standard specification for drinking water (IS:10500). BIS, Manak Bhawan, New Delhi, 2012) for drinking water. Around 95.4% of groundwater samples and 92.3% of leachate samples have high nitrate concentrations above the standard limit of BIS (45 mg/L), respectively. The Piper plot shows that 50% of the samples belong to the Ca²⁺–Mg²⁺–HCO₃⁻ type. Ternary and Durov’s diagrams indicate that the mean concentrations of ions are in the order of Na⁺ > Mg²⁺ > Ca²⁺ > K⁺ (for cations) and HCO₃⁻> NO₃⁻ > Cl⁻ > SO₄²⁻ > CO₃²⁻ > F⁻ (for anions) in groundwater of the study area. The spatial variation of the hydrochemical parameters shows that groundwater is heavily contaminated with respect to nitrate. Analytical results indicate that the groundwater of villages Achheja, Bisrakh road, Dujana, Badalpur and Sadopur is not suitable for drinking.